activity
20172026
most citedDetecting Complex Multi-step Attacks with Explainable Graph Neural Network

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2026

A Blind Trust, the Bloody Thrust: When Attacker-Controlled Hook Updates Steer AI Agent Harnesses towards Malicious Behaviors

Pengxun Li, Litian Zhang, Jianwei Hou +4

Modern AI agent harnesses expose lifecycle hooks that bind shell commands to runtime events such as session start, tool calls, and file edits. These commands run with host privileg…

cs.CR2025

Large Language Models Cannot Reliably Detect Vulnerabilities in JavaScript: The First Systematic Benchmark and Evaluation

Qingyuan Fei, Xin Liu, Song Li +4

Researchers have proposed numerous methods to detect vulnerabilities in JavaScript, especially those assisted by Large Language Models (LLMs). However, the actual capability of LLM…

cs.CR2024

The First Early Evidence of the Use of Browser Fingerprinting for Online Tracking

Zengrui Liu, Jimmy Dani, Yinzhi Cao +2

While advertising has become commonplace in today's online interactions, there is a notable dearth of research investigating the extent to which browser fingerprinting is harnessed…

cs.CR2024★ 1 cited

Detecting Complex Multi-step Attacks with Explainable Graph Neural Network

Wei Liu, Peng Gao, Haotian Zhang +4

Complex multi-step attacks have caused significant damage to numerous critical infrastructures. To detect such attacks, graph neural network based methods have shown promising resu…

cs.CR2017

Deterministic Browser

Yinzhi Cao, Zhanhao Chen, Song Li +1

Timing attacks have been a continuous threat to users' privacy in modern browsers. To mitigate such attacks, existing approaches, such as Tor Browser and Fermata, add jitters to th…